Person re-identification is highly used in the areas of traffic management, searching for lost people, etc. It is hard for existing algorithms to deal with the problem of human pose change, occlusion and feature misalignment, and a pose-guided and feature-fused pedestrian re-recognition algorithm is proposed. The proposed algorithm includes three branches, including global branch, global branch based on pose estimation guidance, and local alignment branch. The global branch extracts the global features of pedestrians and captures the coarse-grained information of pedestrians. The global branch based on posture estimation guidance uses the posture estimation network guidance model to focus on the global visible area of pedestrians and reduce the interference of occlusion to pedestrian recognition. Local alignment branch uitilizes a pose estimation algorithm to establish aligned local features while distinguishing visible local regions to reduce occlusion as well as the influence of postural changes. Through a multi-branch structure, integrated local characteristics with global ones to augment feature diversity is achieved and enhanced model robustness. Finally, network training is conducted using cross-entropy and triplet loss functions. The viability of the proposed algorithm is validated by the test results on Market-1501 and DukeMTMC-ReID datasets, during which the Rank-1 and mAP of the DukeMTMC-ReID dataset reached 91.2% and 81.8%, respectively, which has a better practicality.